Canada's beef exports: Border effects and prospects for market access
Bibliographic record
Abstract
Abstract This paper examines the magnitudes of border effects on Canada's beef exports, and assesses the prospects for market access. The empirical analysis relies on a gravity model derived from a supply‐based framework, and implements different econometric methodologies. It covers the conventional measurement of border effects that is determined relative to the intranational trade baseline. Also, it sets alternative baselines to estimate the wedge between the border effects on beef exports of Canada and those of other countries. The estimated parameters are used to carry out different scenarios to examine the tariff‐related and nontariff border effects, and to evaluate the impacts of trade preferences for Canada's bilateral beef exports. The results reveal significant trade impediments facing Canada's bilateral beef exports to many large markets (e.g., EU‐15, Japan, Republic of Korea, China, and Russia), and they often indicate that the effects of tariff reductions become considerably larger when coupled with reductions in nontariff impediments. Also, they underscore the significance of North American Free Trade Agreement (NAFTA)’s preferential market access for Canada's beef exports. The export opportunities for the Canadian beef industry that are generated through lower trade barriers would, however, decrease when trade barriers facing other beef‐exporting countries are reduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".